Local Probabilistic Model for Bayesian Classification: a Generalized Local Classification Model
This addresses the problem of simplifying probabilistic modeling in Bayesian classification for real-world applications, though it appears incremental as it builds on existing local modeling concepts.
The paper tackles the complexity of modeling probability distributions in the entire sample space for Bayesian classification by proposing local probabilistic models for simpler, more accurate likelihood estimation in local regions, with experimental results showing effectiveness on real-world datasets.
In Bayesian classification, it is important to establish a probabilistic model for each class for likelihood estimation. Most of the previous methods modeled the probability distribution in the whole sample space. However, real-world problems are usually too complex to model in the whole sample space; some fundamental assumptions are required to simplify the global model, for example, the class conditional independence assumption for naive Bayesian classification. In this paper, with the insight that the distribution in a local sample space should be simpler than that in the whole sample space, a local probabilistic model established for a local region is expected much simpler and can relax the fundamental assumptions that may not be true in the whole sample space. Based on these advantages we propose establishing local probabilistic models for Bayesian classification. In addition, a Bayesian classifier adopting a local probabilistic model can even be viewed as a generalized local classification model; by tuning the size of the local region and the corresponding local model assumption, a fitting model can be established for a particular classification problem. The experimental results on several real-world datasets demonstrate the effectiveness of local probabilistic models for Bayesian classification.